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HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTILAYER SUPERPIXEL GRAPH AND LOOPY BELIEF PROPAGATION

HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTILAYER SUPERPIX...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Tianming Zhan Yang Xu Le Sun Zebin Wu Yongzhao Zhan Sch. of Comput. Sci. & Commun. Eng. UJS Zhenjiang China

ISBN: (纸本)9781479979301

In this paper, we propose a new method for hyperspectral image (HSI) classification using multi-layer superpixel graph and loopy belief propagation. A merging algorithm using graph based representation of image is applied to generate multi-scale superpixels in hyperspectral image at first. Then, we build a multi-layer superpixel graph and use loopy belief propagation to transmit messages between the superpixels and compute beliefs at each superpixel in our multi-layer graph for HSI classification. Experimental results with real hyperspectral data set demonstrate that our proposed method provides good performance and is competitive with some of the best available spectral-spatial methods for hyperspectral image classification.

关键词： belief networks geophysical image processing graph theory image classification image representation HSI graph based image representation hyperspectral image classification loopy belief propagation multilayer superpixel graph multiscale superpixels spectral-spatial methods Accuracy Belief propagation Classification algorithms Hyperspectral imaging Image classification Support vector machines hyperspectral classification multi-layer graph multi-scale superpixel Belief propagation Image classification hyperspectral imagery AKR1B10 gene Bayesian networks sorting algorithm image representation Support Vector Network Graph theory Line graph

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Active Learning for Delineation of Curvilinear Structures

Active Learning for Delineation of Curvilinear Structures

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Agata Mosinska-Domanska Raphael Sznitman Przemyslaw Glowacki Pascal Fua EPFL University of Bern

ISBN: (纸本)9781467388528

Many recent delineation techniques owe much of their increased effectiveness to path classification algorithms that make it possible to distinguish promising paths from others. The downside of this development is that they require annotated training data, which is tedious to produce. In this paper, we propose an Active Learning approach that considerably speeds up the annotation process. Unlike standard ones, it takes advantage of the specificities of the delineation problem. It operates on a graph and can reduce the training set size by up to 80% without compromising the reconstruction quality. We will show that our approach outperforms conventional ones on various biomedical and natural image datasets, thus showing that it is broadly applicable.

关键词： Experiential Learning DELINEATION biomedical sorting algorithm

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Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches Using Ensemble Learning & Voted algorithm

Sentiment Classification of Movie Reviews by Supervised Mach...

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International Conference on Data, Engineering and Applications (IDEA)

作者： Rubeena Parveen Neelesh Shrivastava Pradeep Tripathi Vindhya Institute of Technology & Science Satna M.P. India

ISBN: (数字)9781728157184

ISBN: (纸本)9781728157191

In this paper Authors explained their experimental works. In this work we choose movies review from corpora community. corpora contain numbers of dataset from where we selected movies review. In this dataset we have two categories negative & positive each category contains 1000 files. In our file we have movies review by different reviewer. we took. txt file extension. Here we are finding sentiment polarity against those movies. we extend some more in comparison to our base papers. we apply numbers of classification algorithm like NaiveBayes, SkLearn, Support Vector Machine. We applied multiple classifier like MultinomialNB, GaussianNB, BernoulliNB and many more. we select different features (in count or numbers) to understand Accuracy Level of different algorithms. Here Authors used Ensemble Learning where we combined number of classification algorithms and try voted algorithm to find best accuracy.

关键词： Sentiment Analysis StopWord Tokens Features Training & Testing Data Model or classifier Na?ve Bayes SVM SKlearn voted algorithm Ensemble Learning Cinema token Support Vector Network Films Sentiment analysis sorting algorithm Dataset algorithms

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Microwave technology for localization of traumatic intracranial bleedings - a numerical simulation study

Microwave technology for localization of traumatic intracran...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Stefan Candefjord Johan Winges Yinan Yu Thomas Rylander Tomas McKelvey Department of Signals and Systems Chalmers University of Technology

ISBN: (纸本)9781457702150

Traumatic brain injury (TBI) is a major public health problem worldwide. Intracranial bleedings represents the most serious complication of TBI and need to be surgically evacuated promptly to save lives and mitigate injury. Microwave technology (MWT) is promising as a complement to computed tomography (CT) to be used in road and air ambulances for early detection of intracranial bleedings. In this study, we perform numerical simulations to investigate if a classification algorithm based on singular value decomposition can distinguish between bleedings at different positions adjacent to the skull bone for a similar but simplified problem. The classification accuracy is 94-100 % for all classes, a result that encourages us to pursue our efforts with MWT for more realistic scenarios. This indicates that MWT has potential for localizing a detected bleeding, which would increase the diagnostic value of this technique.

关键词： Microwave technology Skull sorting algorithm Air Ambulances numerical simulation Singular value decomposition MEGAWATT THERMAL Hemorrhage diagnostic value Early Diagnosis Road X-Ray Computed Tomography

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RFID Based Tire Classification algorithm Using Support Vector Machines

RFID Based Tire Classification Algorithm Using Support Vecto...

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IEEE International Symposium on Antennas and Propagation

作者： Aaron Brandewie Robert Burkholder ElectroScience Laboratory The Ohio State University Columbus OH USA

ISBN: (数字)9781728166704

ISBN: (纸本)9781728166711

Radio frequency identification (RFID) readers record enormous amounts of data from passive tags located throughout the environment. Due to the large data sets RFID readers collect, machine learning approaches can be used to train robust models. This paper develops an RFID based tire classifier using support vector machines (SVMs). The algorithm correctly identifies all tires in a lab environment and simulations show how the algorithm performs with increased path attenuation.

关键词： RFID machine learning support vector machines Support Vector Network RFID reader Machine Learning path attenuation laboratory environment sorting algorithm

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A Wearable Activity Sensor System and Its Physical Activity Classification Scheme

A Wearable Activity Sensor System and Its Physical Activity ...

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International Joint Conference on Neural Networks

作者： Fang-Chen Chuang Jeen-Shing Wang Ya-Ting Yang Tzu-Ping Kao Department of Electrical Engineering National Cheng Kung University Institute of Education National Cheng Kung University

ISBN: (纸本)9781467314886

This paper presents a wearable activity sensor system and a systematic activity classification scheme for the classification of human daily physical activities. The wearable activity sensor system, consisting of two activity sensor modules worn on users' dominant hand wrists and ankles, is used for collecting activity acceleration signals. The proposed activity classification scheme, including static/dynamic activity analysis, posture recognition, exercise classification, and ambulation classification, is capable of classifying time-series activity acceleration signals. The collected acceleration signals are classify into two categories by means of static/dynamic activity analysis. Posture recognition is applied for partitioning static signals into sitting and standing. Exercise classification and ambulation classification algorithms were used to classify dynamic activity signals. Our experimental results have successfully validated the effectiveness of the proposed wearable sensor system and the scheme of activity classification algorithms with an overall classification accuracy of 96% for seven types of daily activities.

关键词： Physical activity classification Posture recognition Accelerometer K-nearest neighbor classification classification scheme Sensor systems Wearable Ankle Daily Activities Sitting Position signals sorting algorithm activity analysis Motor Activity

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Hardware Implementation of Ground Classification for a Walking Robot

Hardware Implementation of Ground Classification for a Walki...

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Workshop on Robot Motion and Control

作者： Krzysztof Walas Adam Schmidt Marek Kraft Michal Fularz Faculty of Electrical Engineering Poznan University of Technology

ISBN: (纸本)9781467355094

The mobile robot requires the knowledge about the ground type in front of it to efficiently negotiate diverse terrain types while working outdoors. This article presents the implementation of ground classification algorithms in a Field Programmable Gate Array structure. The terrain type classification is based on the signals acquired with force/torque sensor mounted on the walking robot foot. The hardware implementation allows for offloading of the resource demanding computations. The paper begins with a short presentation of the experimental setup. Then the classification algorithms are described. Finally the description of hardware implementation of the algorithms is given followed by the test results.

关键词： walking robots Field programmable gate arrays Computer hardware resource requirements UNLOADING sorting algorithm Mobile robots ground terrains type classification

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A MODELING-BASED CLASSIFICATION algorithm VALIDATED WITH SIMULATED DATA 08

A MODELING-BASED CLASSIFICATION ALGORITHM VALIDATED WITH SIM...

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Winter Simulation Conference (WSC 2008)

作者： Karen Hovsepian Peter Anselmo Subhasish Mazumdar New Mexico Tech Socorro NM

ISBN: (纸本)9781424427086

We present a Generalized Lotka-Volterra (GLV) based approach for modeling and simulation of supervised inductive learning, and construction of an efficient classification algorithm. The GLV equations, typically used to explain the biological world, are employed to simulate the process of inductive learning. In addition, the modeling approach provides a key advantage over the more conventional constraint and optimization-based classification algorithms, as influences of outliers and local patterns, which can lead to problematic over-fitting, are auto-moderated by the model itself. We present the bare-bones algorithm and motivate the model through axiomatic postulates. The algorithm is validated using benchmark simulated datasets, showing results competitive with other cutting-edge algorithms.

关键词： OBSERVATIONAL LEARNING simulated data sorting algorithm GLV axiomatics algorithms outliers simulating Automobiles

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A Power Wheelchair Controlled using Hand Gestures, a Single sEMG Sensor, and Guided Under-determined Source Signal Separation

A Power Wheelchair Controlled using Hand Gestures, a Single ...

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IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Luis A. Rivera Guilherme N. DeSouza Department of Electrical and Computer Engineering University of Missouri

ISBN: (纸本)9781457711992

Surface Electromyographic signals (sEMG) find applications in many areas such as rehabilitation, prosthesis and human-machine interaction. Systems reliant on these muscle-generated electrical signals require some form of machine learning algorithm for recognition of specific patterns of muscle activity. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used, however, in all those cases, the use of multiple sensors is a constant requirement. In this paper, we present a power wheelchair control system that relies on a single sEMG sensor and a new technique for signature recognition called Guided Under-determined Source Signal Separation (GUSSS). Compared to other approaches in the literature, the proposed technique achieves comparable results even when using a simple distance classifier and a very small number of features.

关键词： Electric wheelchair SEMA5B gene Source separation sorting algorithm Machine learning algorithms Sensors Gestures Recognition (Psychology) Feature extraction

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CATE: AN OPEN AND HIGHLY CONFIGURABLE FRAMEWORK FOR PERFORMANCE EVALUATION OF PACKET CLASSIFICATION algorithmS 15

CATE: AN OPEN AND HIGHLY CONFIGURABLE FRAMEWORK FOR PERFORMA...

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Simulation Conference

作者： Wladislaw Gusew Sven Hager Bjorn Scheuermann Humboldt University of Berlin Berlin Germany

ISBN: (纸本)9781467397414

Network packet classification is the central building block for important services such as QoS routing and firewalling. Accordingly, a wide range of classification schemes has been proposed, each with its own specific set of characteristics. But while novel algorithms keep being developed at a high pace, there barely exists tool support for proper benchmarking, which makes it hard for researchers and engineers to evaluate and compare those algorithms in changing scenarios. In this paper, we present the Classification algorithm Testing Environment (CATE). CATE consistently and reproducibly extracts the key performance characteristics, such as memory footprint and matching speed, for a predefined set of classification algorithms from a highly customizable set of benchmarks. In addition, we demonstrate that CATE can be used to gain new insights on both the input parameter sensitivity and the scalability of even well-studied algorithms.

关键词： Performance Evaluation performance characteristics BENCHMARKS Packet algorithms sorting algorithm Scalability Building blocks TEST ENVIRONMENT

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